Jinwei Zhang1, Shun Zhang2, Hersh Patel3, Jacquelyn Knapp4, Gloria Chiang2, David Pisapia2, John Tsiouris2, Linda Heier2, Pascal Spincemaille2, Thanh Nguyen2, Yi Wang2, and Ilhami Kovanlikaya2
1Biomedical Engineering, Cornell University, Ithaca, NY, United States, 2Radiology, Weill Cornell Medical College, New York, NY, United States, 3New York Presbyterian Hospital, New York, NY, United States, 4Cornell University, New York, NY, United States
Synopsis
3D
texture analysis-based feature extraction was deployed on QSM images of malignant
astrocytoma (Anaplastic Astrocytoma (AA), grade III and Glioblastoma (GB), grade
IV) and support vector classifier (SVC) (1) was trained and tested on multiple
training-test dataset splits with different numbers of selected features using
cross-validation (CV). Experiments indicate texture analysis on QSM is useful for differentiating AA and GB
with high accuracy (94.7%).
Target audience
Researchers interested in texture analysis for
brain tumor classification and clinical applications of quantitative
susceptibility mapping (QSM)Introduction
Angiogenic vascular proliferation and necrosis play important roles in grading
astrocytic tumors. More aggressive
tumors tend to have denser and faster-growing tumor blood vessels. Hence, this neovascularization
tends to be leaky, leading to intratumoral hemorrhage. Neovascularization and its
associated micro- and macro-hemorrhages can be measured by QSM (2,3), which is highly sensitive to paramagnetic iron
in venous blood and blood degradation products. Since the treatment regimen and
length of survival are distinctly different between AA and GB patients (4), exploring the effect of QSM texture analysis for
differentiating grade is important for diagnosis and prognosis. In this work,
we extract radiomic features of QSM based on 3D texture analysis and then use
SVC with recursive feature elimination and cross validation (RFECV) to train
the classifier. Our experimental results show high accuracy in differentiating AA
and GB.Methods
58 histopathologically
confirmed patients with newly diagnosed malignant astrocytoma based on 2016 World
Health Organization criteria, including molecular profiling, were included in
this retrospective study (n=23 AA and n=35 GB). Multi-echo gradient echo (GRE)
imaging plus standard T1w and post-contrast T1w, T2FLAIR imaging were acquired
for preoperative MRI tumor imaging. QSM was reconstructed from Multi-echo GRE
data using morphology enabled dipole inversion (MEDI) method (5,6). Regions of interest (ROI) were
defined as the whole tumor volume on post-contrast T1w and T2FLAIR images,
including enhancing and non-enhancing tumor, necrotic and T2 hyperintense areas,
then used as the mask to extract radiomic features from QSM.
107
radiomic features (7) (19 first-order statistical
features, 10 shape-based features, 24 gray level cooccurrence matrix (GLCM)
features, 16 gray level run length matrix (GLRLM) features, 16 gray level size
zone matrix (GLSZM) features, 5 neighboring gray tone difference matrix (NGTDM)
features and 14 gray level dependence matrix (GLDM) features) were extracted
from the ROI on QSM. 10% of patients were randomly selected as test dataset (3 AAs
and 3 GBs), and the remaining 90% were used for training. Synthetic minority over
sampling technique (SMOTE) (8) was applied to increase the
training dataset in a balanced way. After data augmentation, 3-fold cross
validation with recursive feature elimination was used to rank the importance
of all radiomic features. $$$N$$$ most important
features were selected for training and testing SVC classifier. For each pre-defined $$$N$$$, the above process was repeated at 100 times (Figure
2).Results
Figure 1 shows QSMs of representative AA and GB tumors. Hyperintense
intratumoral susceptibility was found to increase with increasing tumor grade. Figure
3 shows average validation accuracy (blue curve) with standard deviation
(shadow) and test accuracy over 100 repetitions at each $$$N$$$. The maximal average validation accuracy (98.0%)
occurred at $$$N=15$$$, and after that, it decreased slightly as $$$N$$$ increased. Maximal average test data accuracy (94.7%)
was also reached at around $$$N=15$$$. For the 15 most important features of two representative
AA and GB cases in the training dataset, figure 4 shows relative feature values of
these two cases with respect to the maximal absolute values of each
corresponding feature among the training dataset. In this radar chart, AA and
GB cases had distinct feature representations and could be easily differentiated
accordingly.Discussion and conclusion
QSM, which does not require a contrast-enhancing agent, could provide additional information
related to the pathological grade in brain tumors. Furthermore, texture analysis using QSM with the ability
to visualize vasculature and hemorrhage can be a useful tool in distinguishing
AA from GB, which is important for treatment and prognosis.Acknowledgements
No acknowledgement found.References
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